ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension

Xiao Zhang, Heyan Huang, Zewen Chi, Xian-Ling Mao


Abstract
Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning; (2) span extraction if required by the above decision; (3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available.
Anthology ID:
2022.coling-1.47
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
570–579
Language:
URL:
https://aclanthology.org/2022.coling-1.47
DOI:
Bibkey:
Cite (ACL):
Xiao Zhang, Heyan Huang, Zewen Chi, and Xian-Ling Mao. 2022. ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension. In Proceedings of the 29th International Conference on Computational Linguistics, pages 570–579, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (Zhang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.47.pdf
Code
 yottaxx/et5
Data
ShARC